Abstract Iron deficiency chlorosis (IDC) is a major yield-limiting factor for soybean production in the mid-western USA. The most practical solution in mitigating losses due to IDC is the development and characterization of IDC tolerant varieties. Leveraging the advanced technique of unmanned aircraft system (UAS) and the thriving deep learning methodology, a convolutional neural network (CNN) could be trained to assist breeders with IDC resistance selection. However, a known difficulty in IDC screening is that the symptoms often vary across diverse genetic backgrounds and spatial or temporal soil heterogeneities. A robust CNN model is desired to mitigate such difficulty. While high robustness usually relies on a sufficiently large labeled training data, the available labeled samples in most breeding programs are normally not enough. Under this limitation, it is critical to find an alternative way to train a robust model. The solution proposed in this study was to apply unsupervised pre-training on the unlabeled aerial images that are much easier to obtain by the UAS. Specifically, a convolutional autoencoder (CAE) was pre-trained on unlabeled sub-images clipped from aerial RGB images; then, the pre-trained weights were reused to initialize the CNN model that was trained on labeled plot-wise sub-images clipped from stitched RGB maps. To test the robustness of this CAE initialized model (CAE1-CNN), two baseline models were equally trained: the first was CAE2-CNN, where the CAE2 was pre-trained with three times of unlabeled data as that of CAE1, by adding wniter wheat and sorghum aerial images; the second was Ran-CNN where the CNN was randomly initialized. Three conditions were considered for testing model robustness: different soybean trials, field locations and vegetative growth stages. Results revealed that both the CAE1-CNN and the CAE2-CNN had relatively better robustness than the Ran-CNN model, i.e., higher R2 and lower RMSE values, especially on different soybean trials and growth stages, which proved that the unsupervised pre-training added gains to the model robustness across diverse trials and growth stages. Similar performances were found between the CAE1-CNN andthe CAE2-CNN model, suggesting that augmenting the unlabled data did not bring significant improvement to model robustness. Additionally, during robustness test on different soybean trials, the unsupervised pre-training seemly showed the potential of alleviating the required number of labeled training samples. These promising findings could contribute to the research on crop stresses by providing a potential path towards developing a robust system for classifying or predicting stress severities under more varied conditions.

中文翻译：

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通过无人飞机系统衍生图像的无监督预训练提高大豆缺铁黄化评级的模型鲁棒性

摘要 缺铁性萎黄病 (IDC) 是美国中西部大豆生产的主要产量限制因素。减轻 IDC 损失的最实用解决方案是开发和表征 IDC 耐受品种。利用无人驾驶飞机系统 (UAS) 的先进技术和蓬勃发展的深度学习方法，可以训练卷积神经网络 (CNN) 以帮助育种者进行 IDC 抗性选择。然而，IDC 筛选的一个已知困难是症状通常因不同的遗传背景和空间或时间的土壤异质性而异。需要一个强大的 CNN 模型来减轻这种困难。虽然高稳健性通常依赖于足够大的标记训练数据，但大多数育种程序中可用的标记样本通常是不够的。在此限制下，找到训练稳健模型的替代方法至关重要。本研究中提出的解决方案是对无人机更容易获得的未标记航拍图像应用无监督预训练。具体来说，卷积自动编码器 (CAE) 是在从航空 RGB 图像中剪下的未标记子图像上进行预训练的；然后，重新使用预先训练的权重来初始化 CNN 模型，该模型在从缝合的 RGB 地图中裁剪的标记的逐图子图像上进行训练。为了测试这个 CAE 初始化模型 (CAE1-CNN) 的鲁棒性，两个基线模型被同等训练：第一个是 CAE2-CNN，其中 CAE2 用三倍于 CAE1 的未标记数据进行预训练，通过添加 wniter小麦和高粱航拍图像；第二个是 Ran-CNN，其中 CNN 是随机初始化的。测试模型稳健性考虑了三个条件：不同的大豆试验、田间位置和营养生长阶段。结果表明，CAE1-CNN 和 CAE2-CNN 都比 Ran-CNN 模型具有相对更好的鲁棒性，即更高的 R2 和更低的 RMSE 值，尤其是在不同的大豆试验和生长阶段，这证明了无监督预训练在不同的试验和成长阶段增加了模型稳健性的收益。在 CAE1-CNN 和 CAE2-CNN 模型之间发现了类似的性能，表明增加未标记数据并没有显着提高模型的鲁棒性。此外，在不同大豆试验的稳健性测试中，无监督的预训练似乎显示出减少所需标记训练样本数量的潜力。